Inheritance diagram for mrob::OptimizerDense:
Collaboration diagram for mrob::OptimizerDense:
Public Member Functions | |
| OptimizerDense (matData_t solutionTolerance=1e-4, matData_t lambda=1e-5) | |
 Public Member Functions inherited from mrob::Optimizer | |
| Optimizer (matData_t solutionTolerance=1e-4, matData_t lambda=1e-5) | |
| uint_t | solve (optimMethod method, uint_t max_iters=1e2, double lambda=1e-5) |
| virtual matData_t | calculate_error ()=0 |
| virtual void | calculate_gradient_hessian ()=0 |
| virtual void | update_state ()=0 |
| virtual void | bookkeep_state ()=0 |
| virtual void | update_state_from_bookkeep ()=0 |
Protected Member Functions | |
| uint_t | optimize_newton_raphson_one_iteration (bool useLambda=false) |
| matData_t | calculate_model_fidelity (matData_t diff_error) |
| Protected Member Functions inherited from mrob::Optimizer | |
| uint_t | optimize_newton_raphson () |
| uint_t | optimize_levenberg_marquardt () |
Protected Attributes | |
| MatX | hessian_ |
| Protected Attributes inherited from mrob::Optimizer | |
| optimMethod | optimization_method_ {} |
| matData_t | solutionTolerance_ |
| uint_t | max_iters_ |
| MatX1 | gradient_ |
| MatX1 | dx_ |
| matData_t | lambda_ |
Additional Inherited Members | |
| Public Types inherited from mrob::Optimizer | |
| enum | optimMethod { NEWTON_RAPHSON =0, LEVENBERG_MARQUARDT_SPHER, LEVENBERG_MARQUARDT_ELLIP } |
Member Function Documentation
◆ calculate_model_fidelity()
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protectedvirtual |
calculate_model_fidely returns the difference between the quadratized model and the current error, as required for the LM algorithm.
This is a an abstrct class since the hessian matrix might be different for the sparse and the dense case
Implements mrob::Optimizer.
◆ optimize_newton_raphson_one_iteration()
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protectedvirtual |
One iteration of the RN method
Implements mrob::Optimizer.
The documentation for this class was generated from the following files:
- src/common/mrob/optimizer.hpp
- src/common/optimizer.cpp
